In a continuing effort to develop potent and selective dihydrofolate reductase (DHFR) inhibitors against opportunistic pathogens, we developed three-dimensional quantitative structure-activity relationship (3D QSAR) models for the inhibitory activity against Pneumocystis carinii (pc) DHFR, Toxoplasma gondii (tg) DHFR, and rat liver DHFR, using a data set of 179 structurally diverse compounds. To ensure a balanced distribution of more potent and less potent drugs in the training set, three different 90-compound training sets taken from the main data set were used, one for each enzyme, while the remaining 89 compounds in the main data set in each case were used as the test set. Three methods, namely, conventional CoMFA, all orientation search (AOS) CoMFA, and CoMSIA were applied to the training sets. While the AOS CoMFA models gave the best internal predictions (cross-validated r(2) values from the training sets), which are satisfactory, CoMSIA models gave the best external predictions (predictive r(2) values from the test sets). Both AOS CoMFA and CoMSIA analyses were used to construct stdev*coefficient contour maps which can be used to design new compounds in an interactive fashion.